Process mining is a field of science bridging the gap between data-oriented analysis and process-oriented analysis, which aims to extract knowledge from event logs [1]}. Process mining techniques are categorized into three types: process discovery, conformance checking, and process enhancement. Process discovery extracts abstract process knowledge using visual process models. Process discovery techniques have been improved to handle complex and large event logs, e.g., Inductive Miner [1]}. However, the application of process discovery techniques in flexible environments such as product development leads to spaghetti process models with an overwhelming number of connections [3]}. One solution is using clustering techniques to group the process instances with similar behavior. Several clustering techniques have been proposed on top of traditional event logs [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [3]}, [12]}, [13]}, [14]}, [15]}, nevertheless, in reality, multiple objects interact with each other in a process [16]}, [17]}, [18]}, [19]}, [20]}, for example, considering a Purchase-to-Pay (P2P) process where orders, items, and customers are involved [21]}, [22]}. Several process discovery techniques have been developed on top of event logs with multiple case notions [23]}, [24]}, [25]}, [26]}, [27]}, [28]}. For example, Object-Centric DFGs (OC-DFGs), used throughout this paper, are one of the object-centric process models developed on top of Object-Centric Event Logs (OCELs). An OC-DFG is a Directly-Follows Graph (DFG) where relations are colored based on object types [23]}. Several examples of such models are shown in the remainder.
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